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A Pytorch implementation of "An Efficient Unfolding Network with Disentangled Spatial-Spectral Representation for Hyperspectral Image Super-Resolution"

EUNet 최신버젼 다운로드

최종 버전 다운로드 (.zip)

// repository documentation

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# An Efficient Unfolding Network with Disentangled Spatial-Spectral Representation for Hyperspectral Image Super-Resolution

Pytorch implementation of EUNet [[Paper](https://doi.org/10.1016/j.inffus.2023.01.018)]

## Requirements
- Python 3.9
- [Pytorch](https://pytorch.org/) 1.12.1

## Training
To train EUNet, run the following commands. You may need to change the `dir_data`, `dataset_name`, `scale`, `n_colors`, `is_blur`, `learning_rate`, etc. in the option.py file for different settings.

```python
# Bicubic downsampling
python main.py --scale 2 --dir_data hdata/data/ --dataset_name Pavia  --n_colors 102

# Gaussian downsampling
python main.py --scale 2 --dir_data hdata/data/ --dataset_name Pavia  --n_colors 102 --is_blur True --learning_rate 1e-3
```

## Testing
For your convience, we provide the testset of Pavia Centre in `/hdata/data/` and the pretrained 2X model in `/hsr/model/`.

```python
python main_test.py --scale 2 --dir_data hdata/data/ --dataset_name Pavia  --n_colors 102 --model_path hsr/model/G.pth
```

## Citation
Please cite our work in your publications if it helps your research.

```BibTex
@article{liu2023efficient,
  title={An Efficient Unfolding Network with Disentangled Spatial-Spectral Representation for Hyperspectral Image Super-Resolution},
  author={Liu, Denghong and Li, Jie and Yuan, Qiangqiang and Zheng, Li and He, Jiang and Zhao, Shuheng and Xiao, Yi},
  journal={Information Fusion},
  year={2023},
  publisher={Elsevier},
  doi={10.1016/j.inffus.2023.01.018}
}
```

## References
- [KAIR](https://github.com/cszn/KAIR)

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